Process control systems, like those used in chemical, petroleum or other processes, typically include one or more process controllers and input/output (I/O) devices communicatively coupled to at least one host or operator workstation and to one or more field devices via analog, digital or combined analog/digital buses. The field devices, which may be, for example, valves, valve positioners, switches and transmitters (e.g., temperature, pressure and flow rate sensors), perform process control functions within the process such as opening or closing valves and measuring process control parameters. The process controllers receive signals indicative of process measurements made by the field devices, process this information to implement a control routine, and generate control signals that are sent over the buses or other communication lines to the field devices to control the operation of the process. In this manner, the process controllers may execute and coordinate control strategies using the field devices via the buses and/or other communication links.
Process information from the field devices and the controllers may be made available to one or more applications (i.e., software routines, programs, etc.) executed by the operator workstation (e.g., a processor-based system) to enable an operator to perform desired functions with respect to the process, such as viewing the current state of the process (e.g., via a graphical user interface), evaluating the process, modifying the operation of the process (e.g., via a visual object diagram), etc. Many process control systems also include one or more application stations (e.g., workstations) that are typically implemented using a personal computer, laptop, or the like and that are communicatively coupled to the controllers, operator workstations, and other systems within the process control system via a local area network (LAN). Each application station may include a graphical user interface that displays the process control information including values of process variables, values of quality parameters associated with the process, process fault detection information, and/or process status information.
Typically, displaying process information in the graphical user interface is limited to the display of a value of each process variable associated with the process. In some cases, process control systems may characterize simple relationships between some process variables to estimate quality metrics associated with the process. However, in most cases where a resultant product of the process does not conform to predefined quality control metrics, the process and/other process variables can generally only be analyzed in detail after the completion of the product.
The use of predictive modeling for process quality prediction and fault detection is beginning to be prevalent in both continuous processes and batch processes. As is known, continuous processes operate in a continuous manner on a set of continuously supplied raw materials to produce an output product. Generally speaking, the process controller(s) used in continuous processes attempt to keep various process parameters the same at particular locations within the process. However, because continuous processes regularly experience variations in, for example, throughput, the types or grades of product being made, the makeup of the raw materials input to the process, etc., it is difficult to perform quality predictions of the output of the process on-line (i.e., while the process is operating) because the process parameter values may change at any particular location based on a change in the throughput, the grade of the product being made, etc. Batch processes, on the other hand, typically operate to process a common set of raw materials together as a “batch” through various numbers of stages or steps, to produce a product. Multiple stages or steps of a batch process may be performed using the same equipment, such as a tank, while others of the stages or steps may be performed in other equipment. However, because the temperature, pressure, consistency, pH, or other parameters of the materials being processed changes over time during the operation of the batch, many times while the material remains in the same location, it is difficult to determine whether the batch process is operating at any particular time during the batch run in a manner that is likely to produce an end product with the desired quality metrics. Thus, it is also difficult to perform quality prediction and fault detection within batch processes.
One known method of predicting whether a currently operating process is progressing normally or within desired specifications (and is thus likely to result in a final product having desired quality metrics) involves comparing various process variable measurements made during the operation of the on-going process with similar measurements taken during the operation of previously run process, the outcome of which has been measured or is otherwise known. However, as noted above, runs of continuous processes vary based on throughput and product grade and runs of batch processes typically vary in temporal length, i.e., vary in the time that it takes to complete the batch, making it difficult to know which time within the previous process run is most applicable to the currently measured parameters of the on-line process. Moreover, in many cases, process variables can vary widely during the operation of the process, as compared to those of a selected previous process, without a significant degradation in quality of the final product. As a result, it is often difficult, if not practically impossible, to identify a particular previous run of the process that is capable of being used in all cases to measure or to predict the quality of subsequent process runs.
A more advanced method of analyzing the results of on-going continuous and batch processes that overcomes one of the problems identified above involves creating a statistical model for the process based on various runs of the process. This technique involves collecting data for each of a set of process variables (parameters) from a number of different runs of a process or for a number of different times in a process and identifying or measuring quality metrics for each of those sets of data. Thereafter, the collected parameters and quality data are used to create a statistical model of the process, with the statistical model representing the “normal” operation of the process that results in desired quality metrics. This statistical model of the process can then be used to analyze how different process parameter measurements made during a particular process implementation statistically relate to the same measurements made within the processes used to develop the model. For example, this statistical model may be used to provide an average or a median value of each measured process parameter, and a standard deviation associated with each measured process variable at any particular time or location during the process run to which the currently measured process variables can be compared. Moreover, this statistical model may be used to predict how the current state of the process will affect or relate to the ultimate quality of the product produced at the end of or at the output of the process.
Generally, both linear and non-linear statistically based process predictors can be used to predict product quality parameters that are not available for on-line measurements. Such process parameter predictors are known by various different names including, for example, soft sensors, inferential sensors and the like. There are, in fact, several types of model based linear predictors that are used to perform process parameter prediction within processes, with the most prevalent of these model based predictors being multiple linear regression (MLR) predictors, principal component regression (PCR) predictors, principal component analysis (PCA) predictors, partial least squares (PLS) predictors and discriminate analysis (DA) predictors. Such predictors can be used in both off-line and on-line analysis tools to predict a process parameter, such as a quality measure of a product being produced by a process. Additionally, it is known to use principle component analysis (PCA) techniques to perform fault detection within processes.
However, known model based predictors have a significant deficiency in that they are generally unable to adjust the predictive process models used therein to the changing process states that may result from, for example, a change in the production rate or throughput of the process, a change in product grades, etc. In fact, to deal with this issue using prior art techniques, it is necessary to construct a separate model for every possible production rate or product grade. However, this technique leads to a predictor that is very complex to build and use, because developing, storing and using the numerous predictive models becomes very processor intensive, requires a lot of memory space and is complex to implement and maintain in real-time systems.
Thus, while it is known to use statistical process modeling techniques to model processes, such as continuous processes, these modeling techniques typically only work well when a continuous process is stable or well-defined, i.e., when there is little variation in the product being made or in the throughput of the process. As a result, the on-line implementation of analytic tools such as PCA and PLS techniques for fault detection and prediction has, in many instances, been limited to continuous processes in which a single product is produced. In such instances, the process is often treated as a single unit with a fixed set of measurements and lab analyses. For these types of processes, a single PCA or PLS model may be developed and applied in an on-line environment. Unfortunately, these techniques do not address the requirements of continuous or batch processes in which multiple grades of products may be produced using one or more different pieces of plant equipment (at different times) or having variable throughputs, or in which other operating conditions are changed regularly.